A deep hybrid learning framework with attention-enhanced feature extraction for BMI prediction based on physical fitness

Background Body Mass Index (BMI) assessment remains a critical challenge in university health monitoring, where traditional methods fail to capture dynamic relationships between physical fitness indicators and body composition. This study develops a novel predictive framework to address this gap through advanced machine learning techniques applied to longitudinal fitness data from Chinese university students. Methods We analyzed 6,698 male students' fitness records (2018–2022) using a hybrid CNN1D-Attention-LightGBM architecture. The model integrates temporal pattern recognition via sliding windows, multi-kernel convolutional operations for physiological coupling analysis, and dynamic attention weighting. Performance was validated through 10-fold cross-validation against SVM and XGBoost benchmarks. Results The model achieved 94.5% accuracy (F1 = 0.93), significantly outperforming conventional methods (XGBoost: 90.1%). Cardiorespiratory endurance (3000 m run, r = 0.2009) and upper-body strength (pull-ups, r = −0.2786) emerged as primary BMI determinants. The framework successfully classified four BMI categories (normal weight: 4,991; obese: 82) . Conclusion This study establishes the first unified solution for fitness-informed BMI prediction, though limited by male-only sampling. Implementation should prioritize integration with campus health systems and expansion to diverse populations. Future work should incorporate psychosocial factors and multi-regional validation.